AI Outcome Customer Engineer, Google Cloud (Portuguese, English)

About the job

As an AI Outcome Customer Engineer in Google Cloud, you work as a technical debugger, engineering liaison, and technical delivery manager. You will bridge the gap between pre-sales agreement shaping and end-to-end post-sales execution, taking complex AI and agentic solutions from prototypes through Minimum Viable Product (MVP) and into scaled production. You will ensure that solutions are shaped strictly through the lens of adoption, rapid consumption activation, measurable business Return on Investment (ROI) and viable delivery.

Working closely alongside our Engineers, and implementation partners, you will architect how technical assets integrate into the customer's IT ecosystem (connectors, identity, data residency, legal constraints) while owning the delivery roadmap, environment readiness, and day-to-day engineering execution required to drive production scale.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Please submit your resume in English - we can only consider applications submitted in this language.

Only applications of candidates with Mexican citizenship will be evaluated for this role in compliance with the provisions of Article 7 of the Federal Labor Law.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Engineering, a related technical field, or equivalent practical experience.
• 5 years of experience in customer-facing technical delivery leadership, technical engagement management, solutions architecture, or enterprise AI/Cloud consulting.
• Experience reading, evaluating, or debugging code in a general-purpose coding language (e.g., Python, Java, JavaScript) and diagnosing architectural blockers.
• Experience in system design or orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI).
• Experience structuring technical delivery roadmaps, managing production deployments, and interfacing with product or engineering organizations.
• Ability to communicate in English and Portuguese fluently to engage with regional enterprise customers and global product teams.

Preferred qualifications:

• Experience leading enterprise Generative AI, LLM-based agentic workflows, or RAG architecture engagements from prototype to production readiness.
• Experience working closely alongside Forward Deployed Engineers (FDEs), software engineering squads, sales and Product/Research teams to build and scale custom technical solutions.
• Proven track record of operating with high autonomy in ambiguous environments, establishing structure, building reusable delivery playbooks, and driving 0→1 initiatives to scale.
• Sequencing instincts with the ability to move fluidly between system-level architecture discussions and execution-level technical detail (e.g., code review, API integrations, evaluation benchmarking).

Responsibilities

• Manage internal and external stakeholders, including customers, providing engagement reports and gathering and escalating client signals and product feedback through internal processes.
• Lead technical onboarding, environment readiness (capacity, quotas, security, IAM/SSO, enterprise connectors, ECMs, and data residency), and organizational change management required to operationalize AI-driven workflows into daily production.
• Embedded with customer engineering and business teams to map workflows and data pipelines, identify integration constraints, shape tools/APIs, and define MVP requirements for GenAI and autonomous agent systems.
• Define impact hypotheses, baseline benchmarks, and measurable KPIs, tracking production consumption velocity, deliverable reliability, and business ROI to report outcomes to C-suite and executive sponsors.
• Structure and own end-to-end delivery roadmaps for multi-workstream AI deployments, defining milestones, technical dependencies, acceptance criteria, and sequencing decisions to protect the critical path while leading technical delivery across engineers.